{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## What's this TensorFlow business?\n",
    "\n",
    "You've written a lot of code in this assignment to provide a whole host of neural network functionality. Dropout, Batch Norm, and 2D convolutions are some of the workhorses of deep learning in computer vision. You've also worked hard to make your code efficient and vectorized.\n",
    "\n",
    "For the last part of this assignment, though, we're going to leave behind your beautiful codebase and instead migrate to one of two popular deep learning frameworks: in this instance, TensorFlow (or PyTorch, if you switch over to that notebook)\n",
    "\n",
    "#### What is it?\n",
    "TensorFlow is a system for executing computational graphs over Tensor objects, with native support for performing backpropogation for its Variables. In it, we work with Tensors which are n-dimensional arrays analogous to the numpy ndarray.\n",
    "\n",
    "#### Why?\n",
    "\n",
    "* Our code will now run on GPUs! Much faster training. Writing your own modules to run on GPUs is beyond the scope of this class, unfortunately.\n",
    "* We want you to be ready to use one of these frameworks for your project so you can experiment more efficiently than if you were writing every feature you want to use by hand. \n",
    "* We want you to stand on the shoulders of giants! TensorFlow and PyTorch are both excellent frameworks that will make your lives a lot easier, and now that you understand their guts, you are free to use them :) \n",
    "* We want you to be exposed to the sort of deep learning code you might run into in academia or industry. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## How will I learn TensorFlow?\n",
    "\n",
    "TensorFlow has many excellent tutorials available, including those from [Google themselves](https://www.tensorflow.org/get_started/get_started).\n",
    "\n",
    "Otherwise, this notebook will walk you through much of what you need to do to train models in TensorFlow. See the end of the notebook for some links to helpful tutorials if you want to learn more or need further clarification on topics that aren't fully explained here."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load Datasets\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "import math\n",
    "import timeit\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train data shape:  (49000, 32, 32, 3)\n",
      "Train labels shape:  (49000,)\n",
      "Validation data shape:  (1000, 32, 32, 3)\n",
      "Validation labels shape:  (1000,)\n",
      "Test data shape:  (10000, 32, 32, 3)\n",
      "Test labels shape:  (10000,)\n"
     ]
    }
   ],
   "source": [
    "from cs231n.data_utils import load_CIFAR10\n",
    "\n",
    "def get_CIFAR10_data(num_training=49000, num_validation=1000, num_test=10000):\n",
    "    \"\"\"\n",
    "    Load the CIFAR-10 dataset from disk and perform preprocessing to prepare\n",
    "    it for the two-layer neural net classifier. These are the same steps as\n",
    "    we used for the SVM, but condensed to a single function.  \n",
    "    \"\"\"\n",
    "    # Load the raw CIFAR-10 data\n",
    "    cifar10_dir = 'cs231n/datasets/cifar-10-batches-py'\n",
    "    X_train, y_train, X_test, y_test = load_CIFAR10(cifar10_dir)\n",
    "\n",
    "    # Subsample the data\n",
    "    mask = range(num_training, num_training + num_validation)\n",
    "    X_val = X_train[mask]\n",
    "    y_val = y_train[mask]\n",
    "    mask = range(num_training)\n",
    "    X_train = X_train[mask]\n",
    "    y_train = y_train[mask]\n",
    "    mask = range(num_test)\n",
    "    X_test = X_test[mask]\n",
    "    y_test = y_test[mask]\n",
    "\n",
    "    # Normalize the data: subtract the mean image\n",
    "    mean_image = np.mean(X_train, axis=0)\n",
    "    X_train -= mean_image\n",
    "    X_val -= mean_image\n",
    "    X_test -= mean_image\n",
    "\n",
    "    return X_train, y_train, X_val, y_val, X_test, y_test\n",
    "\n",
    "\n",
    "# Invoke the above function to get our data.\n",
    "X_train, y_train, X_val, y_val, X_test, y_test = get_CIFAR10_data()\n",
    "print('Train data shape: ', X_train.shape)\n",
    "print('Train labels shape: ', y_train.shape)\n",
    "print('Validation data shape: ', X_val.shape)\n",
    "print('Validation labels shape: ', y_val.shape)\n",
    "print('Test data shape: ', X_test.shape)\n",
    "print('Test labels shape: ', y_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Example Model\n",
    "\n",
    "### Some useful utilities\n",
    "\n",
    ". Remember that our image data is initially N x H x W x C, where:\n",
    "* N is the number of datapoints\n",
    "* H is the height of each image in pixels\n",
    "* W is the height of each image in pixels\n",
    "* C is the number of channels (usually 3: R, G, B)\n",
    "\n",
    "This is the right way to represent the data when we are doing something like a 2D convolution, which needs spatial understanding of where the pixels are relative to each other. When we input image data into fully connected affine layers, however, we want each data example to be represented by a single vector -- it's no longer useful to segregate the different channels, rows, and columns of the data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The example model itself\n",
    "\n",
    "The first step to training your own model is defining its architecture.\n",
    "\n",
    "Here's an example of a convolutional neural network defined in TensorFlow -- try to understand what each line is doing, remembering that each layer is composed upon the previous layer. We haven't trained anything yet - that'll come next - for now, we want you to understand how everything gets set up. \n",
    "\n",
    "In that example, you see 2D convolutional layers (Conv2d), ReLU activations, and fully-connected layers (Linear). You also see the Hinge loss function, and the Adam optimizer being used. \n",
    "\n",
    "Make sure you understand why the parameters of the Linear layer are 5408 and 10.\n",
    "\n",
    "### TensorFlow Details\n",
    "In TensorFlow, much like in our previous notebooks, we'll first specifically initialize our variables, and then our network model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# clear old variables\n",
    "tf.reset_default_graph()\n",
    "\n",
    "# setup input (e.g. the data that changes every batch)\n",
    "# The first dim is None, and gets sets automatically based on batch size fed in\n",
    "X = tf.placeholder(tf.float32, [None, 32, 32, 3])\n",
    "y = tf.placeholder(tf.int64, [None])\n",
    "is_training = tf.placeholder(tf.bool)\n",
    "\n",
    "def simple_model(X,y):\n",
    "    # define our weights (e.g. init_two_layer_convnet)\n",
    "    \n",
    "    with tf.device('/gpu:0'):\n",
    "        # setup variables\n",
    "        Wconv1 = tf.get_variable(\"Wconv1\", shape=[7, 7, 3, 32])\n",
    "        bconv1 = tf.get_variable(\"bconv1\", shape=[32])\n",
    "        W1 = tf.get_variable(\"W1\", shape=[5408, 10])\n",
    "        b1 = tf.get_variable(\"b1\", shape=[10])\n",
    "\n",
    "        # define our graph (e.g. two_layer_convnet)\n",
    "        a1 = tf.nn.conv2d(X, Wconv1, strides=[1,2,2,1], padding='VALID') + bconv1\n",
    "        h1 = tf.nn.relu(a1)\n",
    "        h1_flat = tf.reshape(h1,[-1,5408])\n",
    "        y_out = tf.matmul(h1_flat,W1) + b1\n",
    "        return y_out\n",
    "\n",
    "y_out = simple_model(X,y)\n",
    "\n",
    "# define our loss\n",
    "total_loss = tf.losses.hinge_loss(tf.one_hot(y,10),logits=y_out)\n",
    "mean_loss = tf.reduce_mean(total_loss)\n",
    "\n",
    "# define our optimizer\n",
    "optimizer = tf.train.AdamOptimizer(5e-4) # select optimizer and set learning rate\n",
    "train_step = optimizer.minimize(mean_loss)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "TensorFlow supports many other layer types, loss functions, and optimizers - you will experiment with these next. Here's the official API documentation for these (if any of the parameters used above were unclear, this resource will also be helpful). \n",
    "\n",
    "* Layers, Activations, Loss functions : https://www.tensorflow.org/api_guides/python/nn\n",
    "* Optimizers: https://www.tensorflow.org/api_guides/python/train#Optimizers\n",
    "* BatchNorm: https://www.tensorflow.org/api_docs/python/tf/layers/batch_normalization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Training the model on one epoch\n",
    "While we have defined a graph of operations above, in order to execute TensorFlow Graphs, by feeding them input data and computing the results, we first need to create a `tf.Session` object. A session encapsulates the control and state of the TensorFlow runtime. For more information, see the TensorFlow [Getting started](https://www.tensorflow.org/get_started/get_started) guide.\n",
    "\n",
    "Optionally we can also specify a device context such as `/cpu:0` or `/gpu:0`. For documentation on this behavior see [this TensorFlow guide](https://www.tensorflow.org/tutorials/using_gpu)\n",
    "\n",
    "You should see a validation loss of around 0.4 to 0.6 and an accuracy of 0.30 to 0.35 below"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training\n",
      "Iteration 0: with minibatch training loss = 8.03 and accuracy of 0.11\n",
      "Iteration 100: with minibatch training loss = 1.12 and accuracy of 0.27\n",
      "Iteration 200: with minibatch training loss = 0.588 and accuracy of 0.39\n",
      "Iteration 300: with minibatch training loss = 0.731 and accuracy of 0.3\n",
      "Iteration 400: with minibatch training loss = 0.58 and accuracy of 0.33\n",
      "Iteration 500: with minibatch training loss = 0.478 and accuracy of 0.31\n",
      "Iteration 600: with minibatch training loss = 0.489 and accuracy of 0.39\n",
      "Iteration 700: with minibatch training loss = 0.431 and accuracy of 0.39\n",
      "Epoch 1, Overall loss = 0.743 and accuracy of 0.311\n"
     ]
    },
    {
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YF0uHNt7SsvkHSjmucxw/nzyao+/6EIDrxh7JC19sbNBnvzpvM8d2b0tcbNVE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\nVbnnveX+1S4vGt6dgd3acs1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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7242cc0160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation\n",
      "Epoch 1, Overall loss = 0.439 and accuracy of 0.382\n"
     ]
    }
   ],
   "source": [
    "def run_model(session, predict, loss_val, Xd, yd,\n",
    "              epochs=1, batch_size=64, print_every=100,\n",
    "              training=None, plot_losses=False):\n",
    "    # have tensorflow compute accuracy\n",
    "    correct_prediction = tf.equal(tf.argmax(predict,1), y)\n",
    "    accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))\n",
    "    \n",
    "    # shuffle indicies\n",
    "    train_indicies = np.arange(Xd.shape[0])\n",
    "    np.random.shuffle(train_indicies)\n",
    "\n",
    "    training_now = training is not None\n",
    "    \n",
    "    # setting up variables we want to compute (and optimizing)\n",
    "    # if we have a training function, add that to things we compute\n",
    "    variables = [mean_loss,correct_prediction,accuracy]\n",
    "    if training_now:\n",
    "        variables[-1] = training\n",
    "    \n",
    "    # counter \n",
    "    iter_cnt = 0\n",
    "    for e in range(epochs):\n",
    "        # keep track of losses and accuracy\n",
    "        correct = 0\n",
    "        losses = []\n",
    "        # make sure we iterate over the dataset once\n",
    "        for i in range(int(math.ceil(Xd.shape[0]/batch_size))):\n",
    "            # generate indicies for the batch\n",
    "            start_idx = (i*batch_size)%Xd.shape[0]\n",
    "            idx = train_indicies[start_idx:start_idx+batch_size]\n",
    "            \n",
    "            # create a feed dictionary for this batch\n",
    "            feed_dict = {X: Xd[idx,:],\n",
    "                         y: yd[idx],\n",
    "                         is_training: training_now }\n",
    "            # get batch size\n",
    "            actual_batch_size = yd[idx].shape[0]\n",
    "            \n",
    "            # have tensorflow compute loss and correct predictions\n",
    "            # and (if given) perform a training step\n",
    "            loss, corr, _ = session.run(variables,feed_dict=feed_dict)\n",
    "            \n",
    "            # aggregate performance stats\n",
    "            losses.append(loss*actual_batch_size)\n",
    "            correct += np.sum(corr)\n",
    "            \n",
    "            # print every now and then\n",
    "            if training_now and (iter_cnt % print_every) == 0:\n",
    "                print(\"Iteration {0}: with minibatch training loss = {1:.3g} and accuracy of {2:.2g}\"\\\n",
    "                      .format(iter_cnt,loss,np.sum(corr)/actual_batch_size))\n",
    "            iter_cnt += 1\n",
    "        total_correct = correct/Xd.shape[0]\n",
    "        total_loss = np.sum(losses)/Xd.shape[0]\n",
    "        print(\"Epoch {2}, Overall loss = {0:.3g} and accuracy of {1:.3g}\"\\\n",
    "              .format(total_loss,total_correct,e+1))\n",
    "        if plot_losses:\n",
    "            plt.plot(losses)\n",
    "            plt.grid(True)\n",
    "            plt.title('Epoch {} Loss'.format(e+1))\n",
    "            plt.xlabel('minibatch number')\n",
    "            plt.ylabel('minibatch loss')\n",
    "            plt.show()\n",
    "    return total_loss,total_correct\n",
    "\n",
    "with tf.Session() as sess:\n",
    "    with tf.device(\"/gpu:0\"): #\"/cpu:0\" or \"/gpu:0\" \n",
    "        sess.run(tf.global_variables_initializer())\n",
    "        print('Training')\n",
    "        run_model(sess,y_out,mean_loss,X_train,y_train,1,64,100,train_step,True)\n",
    "        print('Validation')\n",
    "        run_model(sess,y_out,mean_loss,X_val,y_val,1,64)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training a specific model\n",
    "\n",
    "In this section, we're going to specify a model for you to construct. The goal here isn't to get good performance (that'll be next), but instead to get comfortable with understanding the TensorFlow documentation and configuring your own model. \n",
    "\n",
    "Using the code provided above as guidance, and using the following TensorFlow documentation, specify a model with the following architecture:\n",
    "\n",
    "* 7x7 Convolutional Layer with 32 filters and stride of 1\n",
    "* ReLU Activation Layer\n",
    "* Spatial Batch Normalization Layer (trainable parameters, with scale and centering)\n",
    "* 2x2 Max Pooling layer with a stride of 2\n",
    "* Affine layer with 1024 output units\n",
    "* ReLU Activation Layer\n",
    "* Affine layer from 1024 input units to 10 outputs\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# clear old variables\n",
    "tf.reset_default_graph()\n",
    "\n",
    "# define our input (e.g. the data that changes every batch)\n",
    "# The first dim is None, and gets sets automatically based on batch size fed in\n",
    "X = tf.placeholder(tf.float32, [None, 32, 32, 3])\n",
    "y = tf.placeholder(tf.int64, [None])\n",
    "is_training = tf.placeholder(tf.bool)\n",
    "\n",
    "# define model\n",
    "def complex_model(X,y,is_training):\n",
    "\n",
    "    # [1] define parameters\n",
    "    # conv1\n",
    "    Wconv1 = tf.get_variable(\"Wconv1\", shape=[7, 7, 3, 32])\n",
    "    B1 = tf.get_variable(\"Bconv1\",shape=[32])\n",
    "    # ReLU Activation Layer: no parameters\n",
    "    # Spatial Batch Normalization Layer\n",
    "    BN1 = tf.get_variable(\"BN1\",shape=[32])\n",
    "    # Max pool: no parameters\n",
    "    # Affine1    \n",
    "    W1 = tf.get_variable(\"W1\", shape=[5408,1024])\n",
    "    b1 = tf.get_variable(\"B1\",shape=[1024])\n",
    "    # relu: no parameters\n",
    "    # Affine2    \n",
    "    W2 = tf.get_variable(\"W2\", shape=[1024,10])\n",
    "    b2 = tf.get_variable(\"B2\",shape=[10])\n",
    "    \n",
    "    ave_mean = tf.zeros(shape=[32])\n",
    "    ave_var = tf.zeros(shape=[32])\n",
    "\n",
    "    # [2] define our graph\n",
    "    # conv\n",
    "    a1 = tf.nn.conv2d(X, Wconv1, strides=[1,1,1,1], padding='VALID') + B1\n",
    "    # relu\n",
    "    h1 = tf.nn.relu(a1)\n",
    "    # bn\n",
    "    mean, var = tf.nn.moments(h1, [0, 1, 2])\n",
    "    decay = 0.99\n",
    "    ave_mean = decay * mean + (1 - decay) * ave_mean\n",
    "    ave_var = decay * var + (1 - decay) * ave_var\n",
    "    m = tf.cond(is_training, lambda: mean, lambda: ave_mean)\n",
    "    v = tf.cond(is_training, lambda: var, lambda: ave_var)\n",
    "    bn1 = tf.nn.batch_normalization(h1, m, v, BN1, None, 1e-5)\n",
    "    # max pool\n",
    "    h1 = tf.nn.max_pool(bn1,[1,2,2,1],[1,2,2,1],'VALID','NHWC')\n",
    "    # affine\n",
    "    h1_flat = tf.reshape(h1,[-1,5408])\n",
    "    h2 = tf.matmul(h1_flat,W1) + b1\n",
    "    # relu\n",
    "    a2 = tf.nn.relu(h2)\n",
    "    # affine\n",
    "    y_out = tf.matmul(a2,W2) + b2\n",
    "    return y_out\n",
    "\n",
    "y_out = complex_model(X,y,is_training)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To make sure you're doing the right thing, use the following tool to check the dimensionality of your output (it should be 64 x 10, since our batches have size 64 and the output of the final affine layer should be 10, corresponding to our 10 classes):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1000 loops, best of 3: 1.92 ms per loop\n",
      "(64, 10)\n",
      "True\n"
     ]
    }
   ],
   "source": [
    "# Now we're going to feed a random batch into the model \n",
    "# and make sure the output is the right size\n",
    "x = np.random.randn(64, 32, 32,3)\n",
    "with tf.Session() as sess:\n",
    "    with tf.device(\"/cpu:0\"): #\"/cpu:0\" or \"/gpu:0\"\n",
    "        tf.global_variables_initializer().run()\n",
    "\n",
    "        ans = sess.run(y_out,feed_dict={X:x,is_training:True})\n",
    "        %timeit sess.run(y_out,feed_dict={X:x,is_training:True})\n",
    "        print(ans.shape)\n",
    "        print(np.array_equal(ans.shape, np.array([64, 10])))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You should see the following from the run above \n",
    "\n",
    "`(64, 10)`\n",
    "\n",
    "`True`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### GPU!\n",
    "\n",
    "Now, we're going to try and start the model under the GPU device, the rest of the code stays unchanged and all our variables and operations will be computed using accelerated code paths. However, if there is no GPU, we get a Python exception and have to rebuild our graph. On a dual-core CPU, you might see around 50-80ms/batch running the above, while the Google Cloud GPUs (run below) should be around 2-5ms/batch."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100 loops, best of 3: 1.89 ms per loop\n"
     ]
    }
   ],
   "source": [
    "try:\n",
    "    with tf.Session() as sess:\n",
    "        with tf.device(\"/gpu:0\") as dev: #\"/cpu:0\" or \"/gpu:0\"\n",
    "            tf.global_variables_initializer().run()\n",
    "\n",
    "            ans = sess.run(y_out,feed_dict={X:x,is_training:True})\n",
    "            %timeit sess.run(y_out,feed_dict={X:x,is_training:True})\n",
    "except tf.errors.InvalidArgumentError:\n",
    "    print(\"no gpu found, please use Google Cloud if you want GPU acceleration\")    \n",
    "    # rebuild the graph\n",
    "    # trying to start a GPU throws an exception \n",
    "    # and also trashes the original graph\n",
    "    tf.reset_default_graph()\n",
    "    X = tf.placeholder(tf.float32, [None, 32, 32, 3])\n",
    "    y = tf.placeholder(tf.int64, [None])\n",
    "    is_training = tf.placeholder(tf.bool)\n",
    "    y_out = complex_model(X,y,is_training)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You should observe that even a simple forward pass like this is significantly faster on the GPU. So for the rest of the assignment (and when you go train your models in assignment 3 and your project!), you should use GPU devices. However, with TensorFlow, the default device is a GPU if one is available, and a CPU otherwise, so we can skip the device specification from now on."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Train the model.\n",
    "\n",
    "Now that you've seen how to define a model and do a single forward pass of some data through it, let's  walk through how you'd actually train one whole epoch over your training data (using the complex_model you created provided above).\n",
    "\n",
    "Make sure you understand how each TensorFlow function used below corresponds to what you implemented in your custom neural network implementation.\n",
    "\n",
    "First, set up an **RMSprop optimizer** (using a 1e-3 learning rate) and a **cross-entropy loss** function. See the TensorFlow documentation for more information\n",
    "* Layers, Activations, Loss functions : https://www.tensorflow.org/api_guides/python/nn\n",
    "* Optimizers: https://www.tensorflow.org/api_guides/python/train#Optimizers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Inputs\n",
    "#     y_out: is what your model computes\n",
    "#     y: is your TensorFlow variable with label information\n",
    "# Outputs\n",
    "#    mean_loss: a TensorFlow variable (scalar) with numerical loss\n",
    "#    optimizer: a TensorFlow optimizer\n",
    "# This should be ~3 lines of code!\n",
    "mean_loss = None\n",
    "optimizer = None\n",
    "\n",
    "# define our loss\n",
    "cross_entropy = tf.nn.softmax_cross_entropy_with_logits(logits=y_out,labels=tf.one_hot(y,10))\n",
    "mean_loss = tf.reduce_mean(cross_entropy)\n",
    "\n",
    "# define our optimizer\n",
    "optimizer = tf.train.RMSPropOptimizer(1e-3) # select optimizer and set learning rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# batch normalization in tensorflow requires this extra dependency\n",
    "extra_update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)\n",
    "with tf.control_dependencies(extra_update_ops):\n",
    "    train_step = optimizer.minimize(mean_loss)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Train the model\n",
    "Below we'll create a session and train the model over one epoch. You should see a loss of 1.4 to 2.0 and an accuracy of 0.4 to 0.5. There will be some variation due to random seeds and differences in initialization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training\n",
      "Iteration 0: with minibatch training loss = 3.28 and accuracy of 0.16\n",
      "Iteration 100: with minibatch training loss = 2.18 and accuracy of 0.38\n",
      "Iteration 200: with minibatch training loss = 1.34 and accuracy of 0.55\n",
      "Iteration 300: with minibatch training loss = 1.42 and accuracy of 0.47\n",
      "Iteration 400: with minibatch training loss = 1.16 and accuracy of 0.59\n",
      "Iteration 500: with minibatch training loss = 1.34 and accuracy of 0.5\n",
      "Iteration 600: with minibatch training loss = 1.19 and accuracy of 0.59\n",
      "Iteration 700: with minibatch training loss = 1.38 and accuracy of 0.53\n",
      "Epoch 1, Overall loss = 1.66 and accuracy of 0.454\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(1.6573498140646488, 0.4536122448979592)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sess = tf.Session()\n",
    "\n",
    "sess.run(tf.global_variables_initializer())\n",
    "print('Training')\n",
    "run_model(sess,y_out,mean_loss,X_train,y_train,1,64,100,train_step)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Check the accuracy of the model.\n",
    "\n",
    "Let's see the train and test code in action -- feel free to use these methods when evaluating the models you develop below. You should see a loss of 1.3 to 2.0 with an accuracy of 0.45 to 0.55."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation\n",
      "Epoch 1, Overall loss = 1.34 and accuracy of 0.546\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(1.3444550352096558, 0.54600000000000004)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('Validation')\n",
    "run_model(sess,y_out,mean_loss,X_val,y_val,1,64)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train a _great_ model on CIFAR-10!\n",
    "\n",
    "Now it's your job to experiment with architectures, hyperparameters, loss functions, and optimizers to train a model that achieves ** >= 70% accuracy on the validation set** of CIFAR-10. You can use the `run_model` function from above."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Things you should try:\n",
    "- **Filter size**: Above we used 7x7; this makes pretty pictures but smaller filters may be more efficient\n",
    "- **Number of filters**: Above we used 32 filters. Do more or fewer do better?\n",
    "- **Pooling vs Strided Convolution**: Do you use max pooling or just stride convolutions?\n",
    "- **Batch normalization**: Try adding spatial batch normalization after convolution layers and vanilla batch normalization after affine layers. Do your networks train faster?\n",
    "- **Network architecture**: The network above has two layers of trainable parameters. Can you do better with a deep network? Good architectures to try include:\n",
    "    - [conv-relu-pool]xN -> [affine]xM -> [softmax or SVM]\n",
    "    - [conv-relu-conv-relu-pool]xN -> [affine]xM -> [softmax or SVM]\n",
    "    - [batchnorm-relu-conv]xN -> [affine]xM -> [softmax or SVM]\n",
    "- **Use TensorFlow Scope**: Use TensorFlow scope and/or [tf.layers](https://www.tensorflow.org/api_docs/python/tf/layers) to make it easier to write deeper networks. See [this tutorial](https://www.tensorflow.org/tutorials/layers) for how to use `tf.layers`. \n",
    "- **Use Learning Rate Decay**: [As the notes point out](http://cs231n.github.io/neural-networks-3/#anneal), decaying the learning rate might help the model converge. Feel free to decay every epoch, when loss doesn't change over an entire epoch, or any other heuristic you find appropriate. See the [Tensorflow documentation](https://www.tensorflow.org/versions/master/api_guides/python/train#Decaying_the_learning_rate) for learning rate decay.\n",
    "- **Global Average Pooling**: Instead of flattening and then having multiple affine layers, perform convolutions until your image gets small (7x7 or so) and then perform an average pooling operation to get to a 1x1 image picture (1, 1 , Filter#), which is then reshaped into a (Filter#) vector. This is used in [Google's Inception Network](https://arxiv.org/abs/1512.00567) (See Table 1 for their architecture).\n",
    "- **Regularization**: Add l2 weight regularization, or perhaps use [Dropout as in the TensorFlow MNIST tutorial](https://www.tensorflow.org/get_started/mnist/pros)\n",
    "\n",
    "### Tips for training\n",
    "For each network architecture that you try, you should tune the learning rate and regularization strength. When doing this there are a couple important things to keep in mind:\n",
    "\n",
    "- If the parameters are working well, you should see improvement within a few hundred iterations\n",
    "- Remember the coarse-to-fine approach for hyperparameter tuning: start by testing a large range of hyperparameters for just a few training iterations to find the combinations of parameters that are working at all.\n",
    "- Once you have found some sets of parameters that seem to work, search more finely around these parameters. You may need to train for more epochs.\n",
    "- You should use the validation set for hyperparameter search, and we'll save the test set for evaluating your architecture on the best parameters as selected by the validation set.\n",
    "\n",
    "### Going above and beyond\n",
    "If you are feeling adventurous there are many other features you can implement to try and improve your performance. You are **not required** to implement any of these; however they would be good things to try for extra credit.\n",
    "\n",
    "- Alternative update steps: For the assignment we implemented SGD+momentum, RMSprop, and Adam; you could try alternatives like AdaGrad or AdaDelta.\n",
    "- Alternative activation functions such as leaky ReLU, parametric ReLU, ELU, or MaxOut.\n",
    "- Model ensembles\n",
    "- Data augmentation\n",
    "- New Architectures\n",
    "  - [ResNets](https://arxiv.org/abs/1512.03385) where the input from the previous layer is added to the output.\n",
    "  - [DenseNets](https://arxiv.org/abs/1608.06993) where inputs into previous layers are concatenated together.\n",
    "  - [This blog has an in-depth overview](https://chatbotslife.com/resnets-highwaynets-and-densenets-oh-my-9bb15918ee32)\n",
    "\n",
    "If you do decide to implement something extra, clearly describe it in the \"Extra Credit Description\" cell below.\n",
    "\n",
    "### What we expect\n",
    "At the very least, you should be able to train a ConvNet that gets at **>= 70% accuracy on the validation set**. This is just a lower bound - if you are careful it should be possible to get accuracies much higher than that! Extra credit points will be awarded for particularly high-scoring models or unique approaches.\n",
    "\n",
    "You should use the space below to experiment and train your network. The final cell in this notebook should contain the training and validation set accuracies for your final trained network.\n",
    "\n",
    "Have fun and happy training!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Feel free to play with this cell\n",
    "\n",
    "# clear old variables\n",
    "tf.reset_default_graph()\n",
    "\n",
    "X = tf.placeholder(tf.float32, [None, 32, 32, 3])\n",
    "y = tf.placeholder(tf.int64, [None])\n",
    "is_training = tf.placeholder(tf.bool)\n",
    "\n",
    "def batchnorm(y,shift,ave_mean,ave_var,is_training):\n",
    "    mean, var = tf.nn.moments(y, [0, 1, 2])\n",
    "    decay = 0.99\n",
    "    ave_mean = decay * mean + (1 - decay) * ave_mean\n",
    "    ave_var = decay * var + (1 - decay) * ave_var\n",
    "    m = tf.cond(is_training, lambda: mean, lambda: ave_mean)\n",
    "    v = tf.cond(is_training, lambda: var, lambda: ave_var)\n",
    "    bn1 = tf.nn.batch_normalization(y, m, v, shift, None, 1e-5)\n",
    "    return bn1,ave_mean,ave_var\n",
    "\n",
    "def weight_variable_loss(shape,stddev,wl):\n",
    "    var = tf.Variable(tf.truncated_normal(shape,stddev=stddev))\n",
    "    if wl is not None:\n",
    "        weight_loss = tf.multiply(tf.nn.l2_loss(var),wl,name='weight_loss')\n",
    "        tf.add_to_collection('losses',weight_loss)\n",
    "    return var\n",
    "\n",
    "def bias_variable(shape):\n",
    "    return tf.Variable(tf.constant(0.0,shape=shape))\n",
    "\n",
    "def conv2d(x,W,b):\n",
    "    return tf.nn.conv2d(x, W, strides=[1,1,1,1], padding='SAME') + b\n",
    "\n",
    "def max_pool_2x2(x):\n",
    "    return tf.nn.max_pool(x,ksize=[1,2,2,1],strides=[1,2,2,1],padding='VALID',data_format='NHWC')\n",
    "\n",
    "def my_model(X,y,is_training):\n",
    "    \n",
    "    # 1st conv layer\n",
    "    kernel1 = weight_variable_loss(shape=[5,5,3,64],stddev=5e-2,wl=0.0)\n",
    "    k1_bias = bias_variable(shape=[64])\n",
    "    bn1 = bias_variable(shape=[64])\n",
    "    bn1_ave_mean = tf.zeros(shape=[64])\n",
    "    bn1_var_mean = tf.zeros(shape=[64])\n",
    "    \n",
    "    conv1 = conv2d(X, kernel1, k1_bias)\n",
    "    conv1_bn,bn1_ave_mean,bn1_var_mean = batchnorm(conv1,bn1,bn1_ave_mean,bn1_var_mean,is_training)\n",
    "    conv1_relu = tf.nn.relu(conv1_bn)\n",
    "    conv1_pool = max_pool_2x2(conv1_relu)\n",
    "    \n",
    "    # 2nd conv layer\n",
    "    kernel2 = weight_variable_loss(shape=[5,5,64,64],stddev=5e-2,wl=0.0)\n",
    "    k2_bias = bias_variable(shape=[64])\n",
    "#     bn2 = bias_variable(shape=[64])\n",
    "#     bn2_ave_mean = tf.zeros(shape=[64])\n",
    "#     bn2_var_mean = tf.zeros(shape=[64])\n",
    "    \n",
    "    conv2 = conv2d(conv1_pool, kernel2, k2_bias)\n",
    "#     conv2_bn,bn2_ave_mean,bn2_var_mean = batchnorm(conv2,bn2,bn2_ave_mean,bn2_var_mean,is_training)\n",
    "    conv2_relu = tf.nn.relu(conv2)\n",
    "    conv2_pool = max_pool_2x2(conv2_relu)\n",
    "    \n",
    "    # 3rd fc layer\n",
    "    w3 = weight_variable_loss(shape=[8*8*64,384],stddev=0.04,wl=0.005)\n",
    "    b3 = bias_variable(shape=[384])\n",
    "    \n",
    "    conv2_pool_flat = tf.reshape(conv2_pool,[-1,8*8*64])\n",
    "    fc3 = tf.nn.relu(tf.matmul(conv2_pool_flat,w3) + b3)\n",
    "#     fc3_drop = tf.nn.dropout(fc3,0.5)\n",
    "    \n",
    "    # 4th fc layer\n",
    "    w4 = weight_variable_loss(shape=[384,192],stddev=0.04,wl=0.005)\n",
    "    b4 = bias_variable(shape=[192])\n",
    "\n",
    "    fc4 = tf.nn.relu(tf.matmul(fc3,w4) + b4)\n",
    "#     fc4_drop = tf.nn.dropout(fc4,0.5)\n",
    "    \n",
    "    # 5th fc layer\n",
    "    w5 = weight_variable_loss(shape=[192,10],stddev=1/192.0,wl=0.0)\n",
    "    b5 = bias_variable(shape=[10])\n",
    "\n",
    "    y_out = tf.nn.relu(tf.matmul(fc4,w5) + b5)\n",
    "    \n",
    "    return y_out\n",
    "\n",
    "y_out = my_model(X,y,is_training)\n",
    "\n",
    "# define our loss\n",
    "cross_entropy = tf.nn.softmax_cross_entropy_with_logits(logits=y_out,labels=tf.one_hot(y,10))\n",
    "mean_loss = tf.reduce_mean(cross_entropy)\n",
    "total_loss = mean_loss + tf.get_collection('losses')\n",
    "\n",
    "global_step = tf.Variable(0, trainable=False)\n",
    "starter_learning_rate = 1e-3\n",
    "learning_rate = tf.train.exponential_decay(starter_learning_rate, global_step,\n",
    "                                           100000, 0.96, staircase=True)\n",
    "# define our optimizer\n",
    "optimizer = tf.train.RMSPropOptimizer(learning_rate) # select optimizer and set learning rate\n",
    "\n",
    "# batch normalization in tensorflow requires this extra dependency\n",
    "extra_update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)\n",
    "with tf.control_dependencies(extra_update_ops):\n",
    "    train_step = optimizer.minimize(total_loss)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training\n",
      "Iteration 0: with minibatch training loss = 2.3 and accuracy of 0.13\n",
      "Iteration 50: with minibatch training loss = 2.21 and accuracy of 0.23\n",
      "Epoch 1, Overall loss = 2.22 and accuracy of 0.202\n"
     ]
    },
    {
     "data": {
      "image/png": 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jaxNUQ7NSsq+mw31C/AanJ3Ll8cP469XTOX/SINZ0cx2qytpDd0TMTkmgvLq+\nww3GokHEgoeIPCwiu0RkRUDaF0RkpYi0iEhBu+PvEJFiEVkrIucEpJ/rphWLyO2Ryq8xpueMGZDa\nefDYW0N2SsIhS8ED3HLaGEZkJ/M/zy6nqr7zlYVrG5tpaG7pcnb54fi8HmaOyOSLM4awtbyGDbud\n5rSy6hZUOWzwCDR+YH927K9rXWsrVHWNzdQ1thzSbDUgNYEWhb1ROuIqkjWPR4Fz26WtAC4B5gUm\nisgE4HLgWPecv4iI192a9s/AecAE4Ar3WGNMFBs9IIWNe6o7/Na8pby6wyYrcIYb//rSyWyvrOUX\nr67u9Pr72s3I7o7Txzk7O769xtmytqzKyXOwweOYgc6eL2t2hFf76GyNLn9n/ZbyyMxq766IBQ9V\nnYez7Wxg2mpVXdvB4RcBT6lqvapuAoqBme6jWFU3qmoD8JR7rDEmio3OSaGhqYWSDj74tu6tOWhN\nq/YKhmdy/YkjeOLDrcxbt5viXQf49yel/PXdDa3zK/yr+YbT59He4PRExg1M5a3VuwDYXt2CR2BE\ndnIXZzrGDfIHj/A6zTubKe+//6Y9HY9a62vR0mGeB3wQ8LrUTQMoaZd+fGcXEZGbgJsAcnNzKSoq\nCiszVVVVYZ8bC6z8Vv7uln//PmeOx7/fXsi0AW0fM43uboEtB3Yd9h4zE5WXk4RrHl50cN7KNlEw\n0Meqvc71N61dRdGejr6PhmZ0UgOvbjrAK2++Q0lFAzmJHha+Nz+oc1WV1Hh455N1jGjcEvK915Y7\nZdmybjVF5eta05tbFK/AvCWrGVC1IeTrRlq0BI8eoapzgDkABQUFWlhYGNZ1ioqKCPfcWGDlt/J3\nt/zTahv52YdvkJg7gsJTR7WmF++qQt94l1OnT6BwWv5hrzFkwn6eX1LKMQP7M2FQfy57YCH7EnIp\nLJxEzadl8NESTjtxBuMG9u9WXgFSR+zj5fsX0DLgGHbVL2XSsBwKC2cEff7kDR9QWddEYeFJId+7\nYeUOWLSYU2cXMDHv4BFXQ5cU0ZKcSmHh9JCvG2nREjy2AUMCXue7aRwm3RgTpdIS48hJTTik03xr\nJ3M8OjJ+kLOkit+skZm8547C2udvtgpxnkdnpg5JJzM5njdW7WRHtXLhgNSuTwowbmB/nvhwi1Nb\n6GBuyOH4d0TsaIHH4VlJbNpzlPV5hGgucLmIJIjICGAMsAj4CBgjIiNEJB6nU31uH+bTGBOk0TmH\nrnF1uDnbk0J6AAAfWklEQVQeXTlpdDZb9tZQUl7TtoR5D/R5gLNzYuHYHF5bUUZzCCOt/MYNTKWu\nsYUtncyqP5zWLWg7KMvw7GS27K3usZnsPSmSQ3WfBBYCx4hIqYhcLyKfE5FSYDbwioi8DqCqK4Fn\ngFXAa8DNqtqsqk3ALcDrwGrgGfdYY0yUGz0ghQ27qg7aSnXL3hqS471khbHB0UljnJVs56/fQ2Vt\nIwm+4FbUDdbp4wfQ2BzaSCs/f9PZ2jBGXFXUNuD1CKkdDF0ekZ1MTUPzYWea3/3KKv76bu/3iURy\ntNUVqjpIVeNUNV9VH1LVf7vPE1Q1V1XPCTj+blUdparHqOp/AtJfVdWx7nt3Ryq/xpieNTk/jQP1\nTazb1faBurW8hqFZyWHtYDgqJ5lBaf2Yv343FTUNPVbr8Dt5TA4+t8lpVE5wI638xuSm4BFYHUbw\nqHTXterodzI8q+sRVy8tK2Phhr0h37e7oqXZyhgTY04Y7eybsaC47YNty97qww7TPRwR4aTR2SzY\nsJfy6oZuTRDsSFpiHDOGZ5LZT0gNcbvZfnFeRmQns6Ys9OG6ztLyHd/PP1y3s0Um65ua2XmgrstV\ngiPBgocxJiLy0hMZnpXEgg1OJ3dLi1Kyrzas/g6/k8fmUFnbyIebyrvcQTAcP79kEjdPTQjr3HGD\n+gc1UbClRbn1yU/47jNLmbtsOzsq61p3EGxvcHoicV7ptNN8275aVKGpuff7RKJltJUxJgbNHpXN\ny8u209Tcwq4D9TQ0tTC0G8HjxFHOviEH6pp6vNkKnG/6o9LD60cZPzCVV5aXUVXfREoH/Rd+pftq\nmbvMWZDx+SXO4NHCYzremdDrEYZkJrG5k2arEncF4saW3q95WPAwxkTMCaOyeHLRVlZs309tgzMZ\nblgQw3Q7k5WSwLGD+7Ny+/4eG6bbUwI7zacPy+j0uNXuTPSnbpoFCO+t38OskZmdHj8iK7nTZiv/\nDP6+aLay4GGMiZjZbk3h/eI9ZKc4H/bdabYCOGlMthM8knu+5tEdgcuUHC54rN1xABEn2CQn+A57\nLDjDdRds2IuqHtKpXuJunNUXzVbW52GMiZjslATGDUxl4Ya9bNlbg88jDErr161rnjzaaeKJtppH\nXnoiqQm+LpdnX7NjP0MzkzpcVbgjw7OTqW1sZuf+Q4frlpa7zVbWYW6MiTWzR2Xx0eZy1u+qIj8j\nEZ+3ex87M0ZkcM6xuZwwKqvrg3uRiHDMwNQuF0hcs+MA4wYGP4N9xGGG6/prHo1W8zDGxJoTRmVT\n39TCu2t3MzQr/P4OvwSflweuLmDKkPQeyF3PmjY0nWUlla2r/rZX19jM5j3VHBPCelzDs51mvo76\nPfx9Hk1W8zDGxJrjR2biEWhobgl7jseR4qKpeTQ0t/Dy8rIO31+/s4oWdUZmBWtwWiLxPs8hI66q\n6pta9zVp7IPdBi14GGMiqn+/OCblO7WE7naWR7tjB/fnmNxUnl9S2uH7/pFWx4QQPDweYVhm0iHN\nVv5aR3ZKvPV5GGNik79/oqN9y2OJiHDJcXks2VrRYR/F2h0H6BfnYViIzXfDspJbF5X08wePEdnJ\nNtrKGBObzp84iJzUBCblp3V98BHu4ml5eAT+3UHtY82O/YzNTQ152fYR2Uls3lt90CKT/gmCI7NT\nrOZhjIlNk/LT+OiHZzIoLbGvsxJxuf37ceLobJ7/ZNtBH/bg1DxCGWnlNzw7mfqmFnbsr2tNKyl3\nVijOSU2w4GGMMbHg88flU7qvlo82l7em7T5Qz56qhpBGWvn5h+tu2N22P0rpvhqGZCYR5/XQohwS\nqCLNgocxxvSws4/NJTne27p2FbTt9RHKSCu/yUPSifd6eGfN7ta0kvJa8jOS8HmdJrDeXt/Kgocx\nxvSwpHgf500axCuflrVu5LQmjJFWfikJPk4ek83rK3egqqgqJftqGJLprLoLvT9RMJI7CT4sIrtE\nZEVAWqaIvCki692fGW56oYhUishS9/HjgHPOFZG1IlIsIrdHKr/GGNOTbjx5JI3NLXzzySU0Nbew\nZscBclITyEoJb8n3cycOZFtFLctLKymvbqCmoZkhGU6zFfT+RMFI1jweBc5tl3Y78JaqjgHecl/7\nzVfVqe7jpwAi4gX+DJwHTACuEJEJEcyzMcb0iGMGpvKLSybxwcZyfvnaGtbs2B9WZ7nfWRNy8XmE\n/6zY0TrSakhmUutyLzFT81DVeUB5u+SLgMfc548BF3dxmZlAsapuVNUG4Cn3GsYYE/UuOS6fa2YP\n48H5m1i1vXvBIz0pntmjsnhtRVnrHI8hmYnEefzNVr1b8+jtJdlzVdU/b38HkBvw3mwRWQZsB76v\nqiuBPKAk4JhS4PjOLi4iNwE3AeTm5lJUVBRWJquqqsI+NxZY+a38Vv6iHrveKanKgnQPxRUtaMU2\niop2hX2tkXGNzN/bwBNFywHYvGIxG3Y2AfDegoUMSOq9buwug4eIfAt4BDgA/A2YBtyuqm9058aq\nqiLir2ctAYapapWInA+8AIwJ45pzgDkABQUFWlhYGFbeioqKCPfcWGDlt/Jb+Qt79JqTCur47Rtr\n+dq548gOs88D4NgD9Ty++r98tLOFzOR4zj3zNOqXboNPlzJ9xkxG5aT0YK4PL5gw9RVV3Q+cDWQA\nVwP3hHm/nSIyCMD9uQtAVferapX7/FUgTkSygW3AkIDz8900Y4w5YuT278evLp3SrcABkJOawIzh\nmTS3KEMynAmXca19HtHXYe6fR38+8He3OSm0ufVt5gLXus+vBV4EEJGB4m6RJSIz3XztBT4CxojI\nCBGJBy53r2GMMUel8yYOBCDfXSfM5/Z59Pb6VsEEj8Ui8gZO8HhdRFKBLkOciDwJLASOEZFSEbke\np8ZyloisB86krQZzKbDC7fO4D7hcHU3ALcDrwGrgGTd4GWPMUemcY53gMdxdoTjO1zc1j2A6zK8H\npgIbVbVGRDKB67o6SVWv6OStMzo49k/Anzq5zqvAq0Hk0xhjYt7g9ET+fv1Mxg9yljmJ8/TNUN1g\ngsdsYKmqVovIVcBxwB8imy1jjDGdOXlMTutz//Ik0ThJ8H6gRkSmAN8DNgCPRzRXxhhjgtLaYR6F\nCyM2qariTM77k6r+GQh/posxxpgeE9dHNY9gmq0OiMgdOEN0TxYRDxAX2WwZY4wJhs8TvUN1vwjU\n48z32IEz1+LXEc2VMcaYoMT7onRVXTdgPAGkicgFQJ2qWp+HMcZEAX/Noyna9vMQkcuARcAXgMuA\nD0Xk0khnzBhjTNdaN4Nqir6huj8EZqjqLgARyQH+CzwbyYwZY4zpWnzraKsoq3kAHn/gcO0N8jxj\njDER5mvdDCr6ah6vicjrwJPu6y9iM76NMSYqtDZbRdtQXVX9HxH5PHCimzRHVf8d2WwZY4wJRnwf\n7SQY1GZQqvoc8FyE82KMMSZEbavqRknNQ0QOAB2FMsHZy6l/xHJljDEmKN5o24ZWVW0JEmOMiXIi\nQrzXE5VrWxljjIliPq9E5aq6YRORh0Vkl4isCEjLFJE3RWS9+zPDTRcRuU9EikVkuYgcF3DOte7x\n60Xk2o7uZYwxRyufR6JveZJ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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f71e843a668>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 100: with minibatch training loss = 2.12 and accuracy of 0.24\n",
      "Iteration 150: with minibatch training loss = 2.08 and accuracy of 0.29\n",
      "Epoch 2, Overall loss = 1.93 and accuracy of 0.336\n"
     ]
    },
    {
     "data": {
      "image/png": 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apDDDF94rPpLH7eKL79xC70BwzFxEJCdwXLkyl8yUJF6tGR04evuDvHDKWjHf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\nQ4ZzzdGvf7y+k23Lsti8NIv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8egbbqJSaQdsKrHzB08caOFjdSlvPAG+c5WEqx5u3FHGm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\nY9kNZIlI0Qw3WymllG02puOWA43Af4vIJcB+4H6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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f71a0686780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 200: with minibatch training loss = 1.7 and accuracy of 0.41\n",
      "Iteration 250: with minibatch training loss = 1.57 and accuracy of 0.46\n",
      "Epoch 3, Overall loss = 1.61 and accuracy of 0.451\n"
     ]
    },
    {
     "data": {
      "image/png": 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djPprv5aViBnDYdymvQX4tpTyQMa2FYuUUstxlKiqAnjHrl7iSclPX9HulJ85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\nIiKiliSOiIioJYkjIiJq+S94W57kKs0hEQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f71a0727320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 300: with minibatch training loss = 1.5 and accuracy of 0.51\n",
      "Iteration 350: with minibatch training loss = 1.5 and accuracy of 0.51\n",
      "Epoch 4, Overall loss = 1.46 and accuracy of 0.508\n"
     ]
    },
    {
     "data": {
      "image/png": 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5EPNpEAA39DZzcng2Y2U1MB3CG4yxe01TxrZ3bG5j0BPiUhWq2KZMTC5lvG/e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\n6c6U3nBtjk4lqSLpOUn/lF5PlPRM+h58U1J1pMtYFEljJS2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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f71a0724710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 400: with minibatch training loss = 1.41 and accuracy of 0.52\n",
      "Iteration 450: with minibatch training loss = 1.3 and accuracy of 0.55\n",
      "Epoch 5, Overall loss = 1.34 and accuracy of 0.548\n"
     ]
    },
    {
     "data": {
      "image/png": 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CTOtVMoOdxbLTtXLt5i42dXt44mRteYiJQP4B1tFWOQfxXy+M89a/f4pQIo3T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\n/T8qh+i05d0stgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f71c4652c88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 500: with minibatch training loss = 1.14 and accuracy of 0.62\n",
      "Iteration 550: with minibatch training loss = 1.23 and accuracy of 0.58\n",
      "Epoch 6, Overall loss = 1.24 and accuracy of 0.582\n"
     ]
    },
    {
     "data": {
      "image/png": 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3++nZd8wjR4OwBMQcNAhrcXbnlR0A7JxHM5MlzOLJND/dPfcaov/01JELIl+j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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f71c476dc18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 600: with minibatch training loss = 1.06 and accuracy of 0.66\n",
      "Iteration 650: with minibatch training loss = 1.04 and accuracy of 0.67\n",
      "Epoch 7, Overall loss = 1.14 and accuracy of 0.613\n"
     ]
    },
    {
     "data": {
      "image/png": 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BJDMF6ah7hzUDcXSifjdT1GKaXDH1dnRViRJDHS0Ft//nkatp8br504drj0WM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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f71e8455da0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 700: with minibatch training loss = 1.02 and accuracy of 0.67\n",
      "Iteration 750: with minibatch training loss = 1.26 and accuracy of 0.56\n",
      "Epoch 8, Overall loss = 1.06 and accuracy of 0.637\n"
     ]
    },
    {
     "data": {
      "image/png": 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hB59f8JpSipcm5wvyTQVjROLJ1ETsdTkWmIgy2Xt2AoCXb25J297RUJ4GEYol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\nZmY1OUCYmVlN/w/PeVhZJxFBpAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f71c471ef28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 800: with minibatch training loss = 1 and accuracy of 0.65\n",
      "Iteration 850: with minibatch training loss = 0.881 and accuracy of 0.68\n",
      "Epoch 9, Overall loss = 0.979 and accuracy of 0.662\n"
     ]
    },
    {
     "data": {
      "image/png": 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H+fxnwAfNaKabgIkMU9SiYJVfSKYUQ0F9gV+oWIX6/O7CAsIq+10tjC8DAaGUYsRstlRN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\ncy5b1UxSSxoh6XFJqyWtlfSZsP8bkp6XtCr8mRv2S9JXJW2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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f71c46f9f98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 900: with minibatch training loss = 0.817 and accuracy of 0.7\n",
      "Iteration 950: with minibatch training loss = 0.764 and accuracy of 0.73\n",
      "Epoch 10, Overall loss = 0.902 and accuracy of 0.686\n"
     ]
    },
    {
     "data": {
      "image/png": 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s0fjyBftWb0iyHQHxCuCElHJRCPELwO8CtWePrREefHGKW3esoy/kK6tBRJMZ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\nlwOEmZnlcoAwM7Nc/w9xTm9SjL/MqAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f71a0939f28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 1000: with minibatch training loss = 0.882 and accuracy of 0.68\n",
      "Iteration 1050: with minibatch training loss = 0.791 and accuracy of 0.72\n",
      "Epoch 11, Overall loss = 0.84 and accuracy of 0.703\n"
     ]
    },
    {
     "data": {
      "image/png": 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ETpDI5GVD2OkKm1haQMAlOrRuu9XwTOrpcEr+eSa89lILelwMJeGwWbC51w2nzdryyqsz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\n/wcKsdqDb5OuagAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f71a1441a90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 1100: with minibatch training loss = 0.826 and accuracy of 0.69\n",
      "Iteration 1150: with minibatch training loss = 0.784 and accuracy of 0.74\n",
      "Epoch 12, Overall loss = 0.779 and accuracy of 0.721\n"
     ]
    },
    {
     "data": {
      "image/png": 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tyb7+yOkZOB2EX7hhoOa1td6XOpUrYi6Rq9Ig7G06ny6UNYidvUFs6fTL4ZIm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\nS0SkAviZiCwCPgOcBIqAtcCngS8mccy17nksW7ZM6+vrky7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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f71a126a828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 1200: with minibatch training loss = 0.792 and accuracy of 0.71\n",
      "Epoch 13, Overall loss = 0.741 and accuracy of 0.732\n"
     ]
    },
    {
     "data": {
      "image/png": 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muoU1CL0CdeKkHm1kLOYimcmZ8A9ezJqYjo2F2NDXtuC66mv3ZpIFG814KIbb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\ngDAMwzBSYgLCMAzDSIkJCMMwDCMl/w8MYlHvg83V+wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f71c40b6e80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 1250: with minibatch training loss = 0.69 and accuracy of 0.74\n",
      "Iteration 1300: with minibatch training loss = 0.598 and accuracy of 0.77\n",
      "Epoch 14, Overall loss = 0.701 and accuracy of 0.743\n"
     ]
    },
    {
     "data": {
      "image/png": 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LA5d0NSFSfKjrsMcoXbCquQG301b/GkRTpg8CUma0ajNumvsi8UTBk5gvx0rd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\neto5OuTNWByFonGOj/hQCh5/I9VYZ9gT5MSYj9U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1ENC7qdShDK2F8ADhxr2Zk8oTG7bk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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f71a145be80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 1350: with minibatch training loss = 0.572 and accuracy of 0.79\n",
      "Iteration 1400: with minibatch training loss = 0.944 and accuracy of 0.65\n",
      "Epoch 15, Overall loss = 0.684 and accuracy of 0.747\n"
     ]
    },
    {
     "data": {
      "image/png": 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s0z41rwVEUxFPJKui9uULc838TKOxQySeWBbm2uqxTExaQDQb0xm+By0gmozP/GA/v/2v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\n0umBGykztbKYl4+djSbH8w2uCdHLSwfaWDitkikTi5leXcr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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f71a0642ac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 1450: with minibatch training loss = 0.638 and accuracy of 0.75\n",
      "Iteration 1500: with minibatch training loss = 0.565 and accuracy of 0.78\n",
      "Epoch 16, Overall loss = 0.651 and accuracy of 0.754\n"
     ]
    },
    {
     "data": {
      "image/png": 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MSNZV3M+GZATitDHmEWPMUWPMcecn4ytTYr3+xwepgYx0dY3/4majFdE7HEQE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\nS0mKpsrCCf2YTg8GqJlBX625rqbWGMQC5u9vOw9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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f71a1195a58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 1550: with minibatch training loss = 0.741 and accuracy of 0.74\n",
      "Iteration 1600: with minibatch training loss = 1.47 and accuracy of 0.59\n",
      "Epoch 17, Overall loss = 0.664 and accuracy of 0.752\n"
     ]
    },
    {
     "data": {
      "image/png": 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IRHKs9bnWcoO1fWGmx5ZJ4jWIlu6JfZj//PUmLvv6C7FOb5mkoy8wZgmN6iKP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\n4o4LK/InLVlufK2jxsAYEwbWiEgp8Ct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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f71a06426a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 1650: with minibatch training loss = 0.58 and accuracy of 0.77\n",
      "Iteration 1700: with minibatch training loss = 0.614 and accuracy of 0.77\n",
      "Epoch 18, Overall loss = 0.62 and accuracy of 0.762\n"
     ]
    },
    {
     "data": {
      "image/png": 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I7/lXVJXBE+OM+iJMB2PLvZS65i+/f5RPPHx8uZdRMSYCURxWCy3uwulCFWJSVJNiDMSv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\np+dSax5E+kL++t19vP/ObWb8GPINhMdhLeiq7+z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X9HdJqzu/0afXeqLk5cenb9xPSvpG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\nlgOEmZmV5QBhZmZlOUCYmVlZ/wc6kIhbgzRfVwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f71c424ddd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 1750: with minibatch training loss = 0.939 and accuracy of 0.66\n",
      "Iteration 1800: with minibatch training loss = 0.668 and accuracy of 0.75\n",
      "Epoch 19, Overall loss = 0.618 and accuracy of 0.763\n"
     ]
    },
    {
     "data": {
      "image/png": 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5iJkahEWiXJ1dY2NGJdcmQ4OobROTo8LH/zLwccCnf+4AJqWUSf3zALBcf78cOAcgpUwK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\n7GaLyG6f21RLDcqlQRgOfENAnB4L8z9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RNqrrADEckp6JiMW1TketFD3/4Gvg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\na7xR4AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f71ec05b2e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 1850: with minibatch training loss = 0.83 and accuracy of 0.71\n",
      "Iteration 1900: with minibatch training loss = 0.603 and accuracy of 0.75\n",
      "Epoch 20, Overall loss = 0.634 and accuracy of 0.758\n"
     ]
    },
    {
     "data": {
      "image/png": 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YuX5zcadUX4zNGhrE6kUhrhadzZ4aFxD2fBCtVQrTKxXxpCKpsveCsLBMhacn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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f71a1253320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation\n",
      "Epoch 1, Overall loss = 1.29 and accuracy of 0.601\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(1.2920089130401611, 0.60099999999999998)"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Feel free to play with this cell\n",
    "# This default code creates a session\n",
    "# and trains your model for 10 epochs\n",
    "# then prints the validation set accuracy\n",
    "sess = tf.Session()\n",
    "\n",
    "sess.run(tf.global_variables_initializer())\n",
    "print('Training')\n",
    "run_model(sess,y_out,total_loss,X_train,y_train,20,512,50,train_step,True)\n",
    "print('Validation')\n",
    "run_model(sess,y_out,total_loss,X_val,y_val,1,64)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training\n",
      "Epoch 1, Overall loss = 0.399 and accuracy of 0.869\n",
      "Validation\n",
      "Epoch 1, Overall loss = 1.05 and accuracy of 0.706\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(1.0535893239974976, 0.70599999999999996)"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Test your model here, and make sure \n",
    "# the output of this cell is the accuracy\n",
    "# of your best model on the training and val sets\n",
    "# We're looking for >= 70% accuracy on Validation\n",
    "print('Training')\n",
    "run_model(sess,y_out,total_loss,X_train,y_train,1,64)\n",
    "print('Validation')\n",
    "run_model(sess,y_out,total_loss,X_val,y_val,1,64)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Describe what you did here\n",
    "In this cell you should also write an explanation of what you did, any additional features that you implemented, and any visualizations or graphs that you make in the process of training and evaluating your network"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "_Tell us here_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Test Set - Do this only once\n",
    "Now that we've gotten a result that we're happy with, we test our final model on the test set. This would be the score we would achieve on a competition. Think about how this compares to your validation set accuracy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test\n",
      "Epoch 1, Overall loss = 0.946 and accuracy of 0.726\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(0.94615173521041873, 0.72599999999999998)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('Test')\n",
    "run_model(sess,y_out,mean_loss,X_test,y_test,1,64)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Going further with TensorFlow\n",
    "\n",
    "The next assignment will make heavy use of TensorFlow. You might also find it useful for your projects. \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Extra Credit Description\n",
    "If you implement any additional features for extra credit, clearly describe them here with pointers to any code in this or other files if applicable."
   ]
  }
 ],
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